Monthly Archives: August 2013

For one department store, it seemed like business as usual as shoppers perused the racks of merchandise, picking out the latest fashions. But in the security office, the person on duty was alerted to an anomaly — on a merchandising display, an entire section of high-priced jeans had been removed quickly. Was this an overzealous shopper looking to try on multiple pairs, or someone from an organized retail theft ring sweeping up inventory?

In this specific scenario, an officer was made aware of this potential incident through the deployment of video analytics as part of the company’s overall surveillance plan and was able to respond quickly. The retailer had established a set of rules within the system’s software so an alert would be issued if more than half the inventory on that rack was removed in less than one minute — a common scenario known as “shelf sweep” when shoplifters are at work.

The same analytics that are deployed for high-risk security settings, such as airports and government buildings, are equally at home in a retail setting.Like the shelf-sweep rule, similar guidelines can be created so a security officer can be alerted when someone enters a storeroom after hours or when an object, like a package, is left in one place for too long.

But what is equally exciting is that, because of the nature of analytics and its information-gathering abilities, its applications can go well beyond the security realm and become a boon to other store personnel.

Retailers who have included analytics in their security systems to both detect incidents as they happen and aid in forensic investigations of thefts, slip and falls and other activities, are expanding the reach of this investment and applying it to merchandising, marketing and operations.

After all, these cameras are operating 24/7 so why not take this database of information and look at it in the aggregate?

Let’s go back to that department store and see how analytics can help sell some handbags. Data supplied by the point-of-sale system will tell the store operator how many designer purses have sold, but not how many potential sales of those handbags there were on a given day.

By using the video system, the store can track how many people came through the doors (the total pool of potential buyers), and then break it down even further, using rules within the analytics to narrow down how many people walked down the aisle where the handbags were merchandised and then how many of those shoppers lingered for more than five minutes at the display. This information, teamed with the POS data, can now give that store’s manager a conversion rate on the sale of her designer handbags.

Armed with the knowledge of how many bags were sold vs. how many people stopped to look at them, it may mean that the purses are in a great spot or, if the conversion rate is poor, this is an indicator that the bags need to be displayed elsewhere or the signage improved or the price reduced. Analytics won’t read the minds of the shoppers, but the data can provide a good snapshot of what occurred within the store. Using analytics to determine traffic numbers and patterns can aid in where to locate merchandise and even help set the number of checkouts needed on a given day.

From a security standpoint, analytics in video surveillance is a necessary part of doing business, but by expanding the potential of its use, the entire retail operation can benefit — deploying the same equipment, but just tweaking the data to fit each users’ needs. It can be win-win for both security and operations, and who doesn’t like that?

For more on the role of video analytics as part of a retail security solution, downloadour recent white paper on Video Analytics in Retail.

At one time or another, we’ve all experienced information overload as we’ve tried to sort through all the data saved on our computers, stacked on our desks and stored in our smartphones.

That same issue faces loss prevention specialists in the retail industry on a daily basis as they try to make sense of both historical and real-time events and activities captured in Point of Sale (PoS), store management, video, intrusion and access control systems.

This compilation is often referred to, appropriately enough, as Big Data: a heaping pile of bits and bytes that needs to be converted into useful information.

So how do you make sense of Big Data and put it to work for you? Think of it as you would the New York City phone book. If you were looking for someone named John, without having any other parameters with which to find him, you could spend days or even weeks going through all the men named John in the book, checking out each one. But if you know that John’s last name is Smith, you’ve applied a rule that narrowed down the list considerably. It’s still likely to be long, so you look for an opportunity to narrow it even further, and look for all the John Smiths on Lexington Avenue. Now you’ve set the threshold for a workable list.

The same concept of sorting takes place when you apply analytics rules to Big Data. Each rule you add can narrow the data pool until you get to a level where the information is usable and actionable. For example, if you start with the number of people who walk into a store each day, that number may be so large that it does not provide any actionable intelligence. But by applying rules — how many of the total number of people in the store actually made a purchase — you can get to a measurable result. So, if 700 people entered a store on a Sunday, but only ten made a purchase, and only between noon and 1 p.m., you have a result you can address.

Big Data can also be culled to address security issues such as employee theft. You know from applying rules to all your data that you have 100 returns to your store each day. And by analyzing the data even further, you find out that of all the registers in your store, 20 returns are from a particular unit. That raises a red flag. By adding more rules using your video surveillance data, you look at all registers to see when there are returns with no customer present. Now, with this narrowed list, you can identify your likely source of employee theft. Advanced tools provide you with the ability to execute a search from two isolated databases and narrow the results based on the match of the combined data.

By identifying and deploying specific applications to Big Data, you can achieve the goals you want for your business, whether it’s related to improving security, spotting business trends or gathering some other form of usable information.

The key with Big Data is to keep applying rules until you end up with usable, measurable, actionable information. From there, the sky is the limit as to what you’re able to find among all the bits and bytes you’ve stored up over time.

If you’re interested in learning more about the power of data mining and the usability of Big Data, please click here to view our recorded webinar, “Big Data: How to Combat Crime, Spot Business Trends and Determine Real-Time Traffic.”